Submitted to: Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: October 10, 2010
Publication Date: April 1, 2011
Citation: Wu, J., Jenkins, J.N., McCarty Jr., J.C. 2011. A generalized approach and computer tool for quantitative genetics study. Proceedings Applied Statistics in Agriculture, April 25-27, 2010, Manhattan, KS. p.85-106. Interpretive Summary: Quantitative genetic analysis is one of the most important components of plant breeding that provides genetic information for improving productivity and quality of plants and animals. A generalized computer program has been developed for both model development and actual data analysis. This program is a tool that can be used to determine the adequacy of a particular genetic model for a set of data and also to analyze the data. The program uses mixed linear models and can thus handle unbalanced data sets commonly found in plant breeding research. The manuscript describes how to use the model and provides examples of sample output to guide the researcher in its use. It has been validated using several sets of data from cotton breeding and genetic experiments. The most common models used in plant breeding can be analyzed with this computer program. In addition, models that involve seed traits and maternally inherited traits can be analyzed with this tool. The tool provides variance components and predicted genetic effects with standard errors useful for tests of significance.
Technical Abstract: Quantitative genetics is one of the most important components to provide valuable genetic information for improving production and quality of plants and animals. The research history of quantitative genetics study could be traced back more than one hundred years. Since the Analysis of Variance (ANOVA) methods were proposed by Fisher in 1925, several useful genetic models have been proposed and have been widely applied in both plant and animal quantitative genetics studies. Useful examples included various North Carolina (NC) and diallel cross mating designs. However, many genetic models derived from these mating designs are ANOVA method based, so there are several major limitations. For example, ANOVA based methods are constricted to simple genetic models and specific mating designs and require balanced data structures. Though mixed linear model approaches were proposed in the 1960s, their applications in quantitative genetics study were limited until the early 1990s. The advantages of the mixed linear model approaches include the flexibility for unbalanced genetic data structures and complex genetic model systems. In the past years the mixed linear models have been applied to analyze various useful genetic models and a number of computer programs have been developed. In addition, researchers are not only interested in finding appropriate data structures needed for specific genetic models but also want to identify appropriate genetic models suitable for a specific data structure. Therefore, a generalized computer tool has been developed for both model evaluations and actual data analyses. In this paper, various genetic models will be detailed and generalized by mixed linear model approaches and the features of the new computer tool GenMod will be described.